Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. An apparatus comprising: one or more computer readable storage media storing: a sensor grouping data structure configured to cluster correlated sensor channels into contextual groups, each contextual group comprising a set of sensor channels with overlapping outputs that are interchangeable for use in detecting that an at-risk patient or patient on a new medication or treatment has a particular predetermined physical or emotional health-related condition; program instructions that, when executed by one or more processing systems, direct the one or more processing systems to: identify sensor channels associated with a mobile communication device that are available for monitoring; select an initial subset of sensor channels that are available for monitoring and included in one or more of the contextual groups of the sensor grouping data structure; activate the initial subset of sensor channels as active sensor channels; feed outputs of the active sensor channels to a predictive model associated with the particular health-related condition, to infer at least one health-state associated with a user of the mobile communication device; use the contextual groups of correlated sensor channels to minimize output overlap of the active sensor channels by dynamically re-selecting, including deactivating, activating, or both, from the available sensor channels, the active sensor channels in an iterative or recursive manner based on the at least one health-state of the user; and monitor the active sensor channels to detect if the user has the particular predetermined physical or emotional health-related condition.
Wearable and mobile device technology for health monitoring. The invention addresses the challenge of efficiently and accurately detecting specific physical or emotional health conditions in at-risk patients or those undergoing new treatments. It involves an apparatus with computer-readable storage media containing a sensor grouping data structure. This structure clusters sensor channels with overlapping and interchangeable outputs into contextual groups. These groups are used to identify sets of sensors suitable for detecting particular predetermined health conditions. The apparatus also includes program instructions. When executed, these instructions direct a processing system to identify available sensor channels on a mobile communication device. An initial subset of these available sensor channels, belonging to the contextual groups, is selected and activated. The outputs from these active sensor channels are then fed into a predictive model to infer the user's health state. To minimize output overlap among active sensors and improve detection accuracy, the system dynamically re-selects, activates, or deactivates sensor channels iteratively or recursively from the available pool based on the inferred user health state. This process allows for continuous monitoring of active sensor channels to detect the presence of the particular predetermined health condition.
2. The apparatus of claim 1 , wherein to dynamically re-selecting the active sensor channels comprises: responsive to a change in the at least one health-state of the user, predicting sensor channels having outputs with decreased relevance for detecting the particular predetermined physical or emotional health-related condition; and deactivating the sensor channels having outputs with decreased relevance.
This invention relates to a health monitoring apparatus that dynamically adjusts sensor channel selection based on a user's health state to improve detection of specific physical or emotional conditions. The apparatus includes multiple sensors configured to monitor various health-related parameters, such as heart rate, respiration, or stress levels. The system continuously evaluates the relevance of each sensor's output in detecting a predetermined condition, such as hypertension or anxiety, by analyzing the user's health state. When a change in the user's health state occurs, the apparatus predicts which sensor channels will provide less relevant data for detecting the condition. These channels are then deactivated to reduce noise, power consumption, or processing overhead, while maintaining or enhancing the accuracy of condition detection. The dynamic re-selection process ensures that only the most relevant sensors remain active, adapting to real-time changes in the user's physiological or emotional state. This approach optimizes resource usage and improves the reliability of health monitoring by focusing on the most informative sensor inputs.
3. The apparatus of claim 1 , wherein dynamically re-selecting the active sensor channels comprises: responsive to a change in the at least one health-state of the user, predicting sensor channels having outputs with increased relevance for detecting the particular predetermined physical or emotional health-related condition; and activating the sensor channels having outputs with increased relevance while using the contextual groups to minimize the output overlap of the active sensor channels.
This invention relates to a health monitoring apparatus that dynamically adjusts sensor channel selection based on a user's health state to improve detection of specific physical or emotional conditions. The apparatus includes multiple sensors that generate outputs, which are grouped into contextual groups to reduce overlap and interference. The system monitors the user's health state, such as vital signs or emotional indicators, and dynamically re-selects active sensor channels when changes in the health state occur. Upon detecting a change, the apparatus predicts which sensor channels will provide more relevant outputs for detecting the target health condition. It then activates those channels while leveraging the contextual grouping to minimize output overlap, ensuring more accurate and efficient monitoring. The dynamic re-selection process ensures that the most relevant sensor data is prioritized, improving the system's ability to detect and analyze the user's health condition in real time. This approach enhances the reliability and precision of health monitoring by adapting to the user's changing state without requiring manual adjustments.
4. The apparatus of claim 1 , wherein to dynamically re-selecting the active sensor channels comprises: identifying status information associated with the mobile communication device, wherein the active sensor channels are re-selected based on the status information associated with the mobile communication device.
This invention relates to a system for dynamically managing sensor channels in a mobile communication device to optimize performance and power efficiency. The problem addressed is the inefficient use of sensor resources in mobile devices, where fixed sensor configurations may lead to unnecessary power consumption or degraded performance under varying operational conditions. The apparatus includes a sensor management module that monitors and adjusts active sensor channels based on real-time status information of the mobile device. The status information may include factors such as device activity, environmental conditions, user behavior, or power levels. By dynamically re-selecting active sensor channels, the system ensures that only relevant sensors are operational, reducing power drain and improving overall efficiency. The dynamic re-selection process involves continuously evaluating the device's status to determine which sensors are needed. For example, if the device is in a low-power mode, non-essential sensors may be deactivated. Conversely, if the device detects motion or user interaction, additional sensors may be activated to enhance functionality. This adaptive approach ensures optimal sensor utilization without manual intervention, extending battery life and improving device responsiveness. The invention enhances existing mobile communication devices by introducing an intelligent sensor management system that automatically adjusts sensor activity based on real-time conditions, addressing inefficiencies in traditional fixed-configuration approaches.
5. The apparatus of claim 4 , wherein the status information associated with the mobile communication device comprises information relating to power consumption of one or more sensor channels or battery status information associated with the mobile communication device.
This invention relates to an apparatus for monitoring and managing the status of a mobile communication device, particularly focusing on power consumption and battery status. The apparatus includes a processor and a memory storing instructions that, when executed, enable the device to collect and analyze status information related to power consumption across one or more sensor channels or battery status. The system is designed to optimize power usage by tracking how different sensors or components of the mobile device consume energy, allowing for adjustments to improve efficiency. Additionally, the apparatus may monitor battery health, charge levels, and discharge rates to provide insights into the device's operational state. By integrating these monitoring capabilities, the apparatus helps users and applications make informed decisions to extend battery life and enhance device performance. The invention addresses the need for efficient power management in mobile devices, where battery life is a critical factor for user experience and functionality. The apparatus may also include communication interfaces to transmit this status information to external systems for further analysis or control.
6. The apparatus of claim 4 , wherein to select the initial subset of sensor channels from the available sensor channels, the instructions, when executed by the one or more processing systems, direct the one or more processing systems to: identify sensor channels that provide overlapping outputs from the available sensor channels; and select sensor channels for the initial subset of sensor channels having outputs that do not overlap.
This invention relates to sensor data processing, specifically optimizing sensor channel selection to reduce redundancy. The problem addressed is the inefficiency in systems where multiple sensors provide overlapping or redundant data, leading to unnecessary computational overhead and potential noise accumulation. The invention improves sensor data processing by intelligently selecting a subset of sensor channels that minimize overlap in their outputs, thereby enhancing data quality and processing efficiency. The apparatus includes one or more processing systems configured to execute instructions for selecting an initial subset of sensor channels from available sensor channels. The selection process involves identifying sensor channels that produce overlapping outputs and then choosing those channels whose outputs do not overlap. This ensures that the selected subset provides diverse and non-redundant data, improving the accuracy and reliability of subsequent processing steps. The apparatus may also include additional components, such as memory for storing sensor data and interfaces for receiving and transmitting data, to support the selection and processing of sensor channels. By reducing redundancy in sensor data, the invention optimizes resource usage and enhances the performance of systems relying on sensor inputs, such as environmental monitoring, industrial automation, or autonomous navigation. The method ensures that only the most relevant and non-redundant sensor data is processed, leading to more efficient and accurate system operation.
7. The apparatus of claim 1 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: detect the particular predetermined physical or emotional health-related condition based on the outputs of the active sensor channels; identify a clinician associated with the user of the mobile communication device; and in response to detecting the particular predetermined health-related condition, generate and transmit a notification to a communication device or a communication interface associated with the clinician.
This invention relates to a health monitoring system that detects physical or emotional health conditions using sensor data from a mobile communication device and automatically notifies a clinician. The system includes a mobile device with active sensor channels, such as accelerometers, gyroscopes, or biometric sensors, that collect health-related data from the user. The device processes this data to detect specific health conditions, such as abnormal heart rates, stress levels, or physical injuries. Upon detection, the system identifies a clinician associated with the user and sends a notification to the clinician's communication device or interface, enabling timely medical intervention. The system may also include a user interface for configuring sensor settings, health condition thresholds, and clinician contact information. The invention improves healthcare responsiveness by leveraging mobile device sensors and automated alerts, reducing the need for manual monitoring and ensuring faster clinician notification in critical situations.
8. The apparatus of claim 1 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: detect the particular predetermined physical or emotional health-related condition based on the outputs of the active sensor channels; responsive to detecting the particular predetermined health-related condition, send an indictor to a health monitoring platform, wherein the indicator indicates that the predetermined health-related condition was detected.
This invention relates to a health monitoring apparatus designed to detect and report specific physical or emotional health-related conditions using sensor data. The apparatus includes one or more processing systems configured to execute instructions for analyzing outputs from multiple active sensor channels. These sensors monitor physiological or behavioral indicators associated with health conditions. The system processes the sensor data to identify a predetermined health-related condition, such as abnormal heart rate, stress, or fatigue. Upon detection, the apparatus sends an indicator to a health monitoring platform, alerting it to the identified condition. The health monitoring platform may then take further action, such as notifying a healthcare provider or adjusting treatment protocols. This system enhances real-time health monitoring by automating condition detection and ensuring timely communication of health status to relevant platforms. The apparatus is particularly useful in wearable or remote monitoring devices, where continuous health tracking is required. The invention improves upon prior systems by integrating multiple sensor inputs and providing direct, automated alerts to centralized health monitoring systems, reducing response times and improving patient care.
9. The apparatus of claim 1 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: encrypt or encode at least one of the outputs of the active sensor channels; and send the at least one encoded output to a health monitoring platform.
This invention relates to a health monitoring system that processes sensor data from active sensor channels. The system addresses the challenge of securely transmitting health data from wearable or implantable medical devices to a remote health monitoring platform. The apparatus includes one or more processing systems configured to receive and process outputs from multiple active sensor channels, which may include physiological sensors such as heart rate monitors, blood pressure sensors, or glucose level sensors. The processing systems execute instructions to encrypt or encode the sensor outputs to ensure data security during transmission. The encoded outputs are then sent to a health monitoring platform, which may be a cloud-based or centralized system for analyzing and storing patient health data. The encryption or encoding step prevents unauthorized access to sensitive health information during transmission. The system may also include additional processing steps, such as filtering or normalizing the sensor data before encryption, to improve data quality and reliability. The health monitoring platform can then use the received data for real-time or batch analysis, alerting healthcare providers to potential health issues or trends. This invention enhances the security and efficiency of remote health monitoring systems.
10. The apparatus of claim 1 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: refine the predictive model based on the outputs of the active sensor channels.
This invention relates to a system for refining predictive models using active sensor channels. The system addresses the challenge of improving the accuracy and reliability of predictive models by dynamically adjusting them based on real-time sensor data. The apparatus includes one or more processing systems configured to execute instructions for processing sensor data from multiple sensor channels. The system identifies active sensor channels, which are those currently providing valid or relevant data, and uses their outputs to refine the predictive model. This refinement process involves updating the model parameters or structure to better align with the observed sensor data, thereby enhancing prediction accuracy. The system may also include mechanisms for handling sensor failures or data inconsistencies by dynamically adjusting which channels are considered active. The overall goal is to ensure the predictive model remains accurate and reliable over time, even as sensor conditions or environmental factors change. This approach is particularly useful in applications where sensor data is critical for decision-making, such as industrial monitoring, autonomous systems, or environmental sensing.
11. The apparatus of claim 1 , wherein to identify the available sensor channels, the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: process device attribute information or device status information associated with the mobile communication device, wherein the available sensor channels associated with the mobile communication device are identified based on the device attribute information or the device status information associated with the mobile.
A system designed for healthcare monitoring identifies which sensor channels on a mobile communication device are available for use. This system utilizes a data structure that organizes correlated sensor channels into "contextual groups." Each group contains multiple sensors with overlapping outputs that are interchangeable for detecting a specific physical or emotional health condition in an at-risk patient or one on new medication/treatment. To identify these available sensor channels, the system's software processes device attribute information (e.g., the types of sensors physically present, their capabilities) or current device status information (e.g., sensor operational status, power consumption, battery level). The system then determines which sensor channels are actively available for monitoring based on this processed attribute or status data. ERROR (embedding): Error: Failed to save embedding: Could not find the 'embedding' column of 'patent_claims' in the schema cache
12. The apparatus of claim 1 , wherein the sensor grouping data structure identifies groups of sensor channels that provide overlapping outputs for use in detecting each of multiple different physical or emotional health-related conditions in a layered or hierarchical format.
This invention relates to a health monitoring apparatus that uses sensor data to detect multiple physical or emotional health-related conditions. The apparatus includes a sensor grouping data structure that organizes sensor channels into groups, where each group provides overlapping outputs for detecting specific health conditions. The grouping is structured in a layered or hierarchical format, allowing the system to analyze sensor data at different levels of granularity. This hierarchical approach enables the apparatus to distinguish between different health conditions by leveraging redundant or complementary sensor inputs. The sensor channels may include physiological, environmental, or behavioral sensors, and the overlapping outputs help improve accuracy and reliability in condition detection. The layered structure allows for efficient processing, where higher-level groups may represent broader health categories, while lower-level groups focus on more specific conditions. This design enhances the system's ability to monitor complex health states by dynamically adjusting sensor groupings based on detected patterns or user-specific data. The apparatus may be used in wearable or embedded health monitoring systems to provide real-time or continuous health assessments.
13. The apparatus of claim 1 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: dynamically adjust a sampling rate of one or more of the active sensor channels in an iterative or recursive manner based on one or more of the at least one health-state of the user or status information associated with the mobile communication device.
This invention relates to a system for dynamically adjusting sensor sampling rates in a mobile communication device based on user health-state data or device status information. The system includes one or more processing systems configured to execute instructions for monitoring active sensor channels, such as those measuring physiological or environmental parameters. The sampling rate of these sensors is dynamically adjusted in an iterative or recursive manner to optimize performance, power consumption, or data accuracy. The adjustments are based on real-time health-state data of the user, such as heart rate, activity level, or stress indicators, or device status information, such as battery level, network connectivity, or processing load. By dynamically modifying the sampling rate, the system ensures efficient resource utilization while maintaining accurate and relevant data collection. This approach is particularly useful in wearable or mobile devices where power efficiency and real-time data processing are critical. The iterative or recursive adjustment process allows the system to continuously adapt to changing conditions, ensuring optimal performance without manual intervention.
14. An apparatus comprising: one or more computer readable storage media storing program instructions that, when executed by one or more processing systems, direct the one or more processing systems to: collect sensor data from multiple sensor channels associated with multiple communication devices; process the sensor data to identify training users having a predetermined health-related condition; correlate the sensor data associated with the training users having the predetermined health-related condition to identify sensor channels that provide information that is relevant for use in detecting that an at-risk patient or patient on a new medication or treatment has the predetermined health-related condition; correlate the sensor channels that provide information that is relevant for use in detecting the predetermined health-related condition to identify sensor channels that provide overlapping outputs that are interchangeable for use in detecting that the at-risk patient or patient on a new medication or treatment has the predetermined physical or emotional health-related condition; cluster the sensor channels that provide the overlapping sensor data into multiple contextual sensor groups associated with the predetermined physical or emotional health-related condition, each sensor group comprising a set of sensor channels with overlapping outputs; and generate a sensor grouping data structure that facilitates dynamic selection of active sensor channels with minimal or no overlap in outputs using the contextual sensor groups associated with the predetermined physical or emotional health-related condition.
This invention relates to a system for analyzing sensor data from multiple communication devices to detect health-related conditions in patients. The system collects sensor data from various sensor channels associated with these devices, processing the data to identify training users who exhibit a predetermined health-related condition. By analyzing the sensor data of these training users, the system determines which sensor channels provide relevant information for detecting the condition in at-risk patients or those undergoing new treatments. The system further identifies sensor channels that produce overlapping outputs, meaning their data can be used interchangeably for detecting the condition. These overlapping sensor channels are then grouped into contextual sensor clusters, where each cluster contains a set of sensors with similar outputs. The system generates a data structure that enables dynamic selection of active sensor channels, ensuring minimal or no overlap in their outputs. This approach optimizes sensor usage for accurate and efficient health condition detection while reducing redundancy. The system is designed to adapt to different health conditions and patient scenarios, improving diagnostic capabilities in healthcare applications.
15. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: process the sensor data to identify training users having a second predetermined physical or emotional health-related condition; correlate the sensor data associated with the training users having the second predetermined physical or emotional health-related condition to identify sensor channels that provide information that is relevant for use in detecting that the at-risk patient or patient on a new medication or treatment has the second predetermined physical or emotional health-related condition; correlate the sensor channels that provide information that is relevant for use in detecting the second predetermined physical or emotional health-related condition to identify sensor channels that provide overlapping outputs that are interchangeable for use in detecting that the at-risk patient or patient on a new medication or treatment has the second predetermined physical or emotional health-related condition; and append the sensor grouping data structure by clustering the sensor channels that provide the overlapping sensor data that is relevant for detecting the second predetermined physical or emotional health-related condition into multiple sensor groups associated with the second predetermined physical or emotional health-related condition.
This invention relates to a system for monitoring and analyzing sensor data to detect physical or emotional health conditions in patients. The system processes sensor data from multiple sources to identify training users with a specific health condition, then correlates the sensor data to determine which sensor channels provide relevant information for detecting that condition. The system further analyzes these sensor channels to identify overlapping outputs that can be used interchangeably for detection purposes. Finally, the system organizes the relevant sensor channels into multiple groups, clustering those with overlapping data to improve detection accuracy for the target health condition. This approach allows the system to adapt to different patients, including those at risk or undergoing new treatments, by leveraging interchangeable sensor inputs to enhance condition monitoring. The system dynamically groups sensors based on their relevance and redundancy, ensuring robust and flexible health condition detection.
16. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: process the sensor data collected from the relevant sensor channels to generate a predictive model configured to infer a health-state of a user associated with a mobile communication device based on outputs of active sensor channels provided by the mobile communication device.
This invention relates to a system for monitoring user health using sensor data from mobile communication devices. The problem addressed is the need for accurate and continuous health-state inference without requiring specialized medical equipment. The apparatus includes a processing system that collects sensor data from multiple channels on a mobile device, such as motion sensors, environmental sensors, or biometric sensors. The system processes this data to generate a predictive model that infers the health-state of the user. The model is trained on outputs from active sensor channels, allowing it to adapt to available data sources. The apparatus may also include a communication interface for transmitting the inferred health-state to a remote server or another device. The predictive model can be updated dynamically based on new sensor data, improving accuracy over time. The system may also filter or preprocess the sensor data to remove noise or irrelevant information before model generation. The overall goal is to provide a scalable, non-invasive health monitoring solution using existing mobile device sensors.
17. The apparatus of claim 16 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: identify sensor channels associated with a mobile communication device that are available for monitoring; select an initial subset of sensor channels that are available for monitoring and included in one or more of the contextual groups of the sensor grouping data structure; activate the initial subset of sensor channels as active sensor channels to monitor the active sensor channels; feed outputs of the active sensor channels to a predictive model configured to infer a health-state of a user associated with the mobile communication device based on the outputs; and using the contextual groups of correlated sensor channels to maintain minimal or no overlap in outputs of the active sensor channels, dynamically re-select, including deactivating, activating, or both, from the available sensor channels, the active sensor channels in an iterative or recursive manner based on the health-state of the user.
This invention relates to a system for monitoring a user's health state using sensor data from a mobile communication device. The problem addressed is the efficient and accurate collection of sensor data to infer health states while minimizing redundancy and computational overhead. The system identifies sensor channels available on the mobile device, such as accelerometers, gyroscopes, or biometric sensors, and groups them into contextual categories based on their relevance to different health states. An initial subset of these sensor channels is activated to collect data, which is then fed into a predictive model to infer the user's health state. The system dynamically adjusts the active sensor channels by deactivating or activating others based on the inferred health state, ensuring minimal overlap in sensor outputs. This dynamic selection process is iterative or recursive, continuously optimizing sensor usage to maintain accuracy while reducing unnecessary data collection. The contextual grouping of sensor channels ensures that only the most relevant sensors are active at any given time, improving efficiency and battery life.
18. A computing system comprising: one or more processing systems; and one or more computer readable storage media storing: a sensor grouping data structure that clusters sensor channels into contextual groups each contextual group comprising a set of sensor channels with overlapping outputs that are interchangeable for use in detecting that an at-risk patient or patient on a new medication or treatment has a predetermined physical or emotional health-related condition; program instructions that, when executed by one or more processing systems, direct the one or more processing systems to: activate an initial subset of sensor channels as active sensor channels, wherein the initial subset of sensor channels is selected from a group of available sensor channels that are also included in one or more of the contextual groups of the sensor grouping data structure; feed outputs of the active sensor channels to a predictive model associated with the predetermined physical or emotional health-related condition to infer a health-state of a user associated with a mobile communication device; use the contextual groups of correlated sensor channels to minimize output overlap of the active sensor channels by dynamically re-selecting, including deactivating, activating, or both, from the available sensor channels, the active sensor channels in an iterative or recursive manner based on the health-state of the user; and monitor the active sensor channels to detect if the user has the predetermined physical or emotional health-related condition.
The computing system is designed for health monitoring, particularly for detecting at-risk patients or those undergoing new treatments. The system addresses the challenge of efficiently processing sensor data to identify health conditions by dynamically managing sensor inputs to reduce redundancy and improve accuracy. It includes a sensor grouping data structure that organizes sensor channels into contextual groups, where each group contains interchangeable sensors that produce overlapping outputs relevant to a specific health condition. The system initially activates a subset of these sensors, feeding their outputs into a predictive model to assess the user's health state. To optimize performance, the system dynamically adjusts the active sensors by activating, deactivating, or re-selecting them based on the user's health state, minimizing redundant data while maintaining accuracy. This iterative process ensures continuous monitoring for the predetermined health condition. The system is particularly useful in mobile health applications, where efficient sensor management is critical for real-time health assessments.
19. The computing system of claim 18 , further comprising: a plurality of sensors or apparatuses operatively coupled with the one or more processing systems, wherein each sensor or apparatus provides an output or indicator when a corresponding sensor channel is activated.
This invention relates to computing systems designed for monitoring and controlling sensor-based operations. The system includes one or more processing systems configured to manage sensor channels, where each channel corresponds to a specific sensor or apparatus. The processing systems activate these channels based on predefined conditions or user inputs, enabling real-time data collection or control signals. The system further includes a plurality of sensors or apparatuses operatively coupled to the processing systems. Each sensor or apparatus generates an output or indicator when its corresponding channel is activated, allowing the system to monitor environmental conditions, device states, or other parameters. The processing systems may also process the outputs to trigger actions, log data, or adjust system behavior dynamically. This setup ensures efficient and responsive interaction with external devices, improving automation and monitoring capabilities in various applications, such as industrial control, environmental monitoring, or smart infrastructure. The system may also include interfaces for user interaction, enabling manual activation or configuration of sensor channels. The overall design enhances flexibility and scalability in sensor-based computing environments.
20. The computing system of claim 18 , wherein the instructions, when executed by the one or more processing systems, further direct the one or more processing systems to: dynamically adjust a sampling rate of one or more of the active sensor channels in an iterative or recursive manner based on the health-state of the user.
The invention relates to computing systems for monitoring user health using sensor data. The system addresses the challenge of efficiently collecting and processing sensor data to assess a user's health state while optimizing computational and power resources. The system includes multiple active sensor channels that collect physiological data, such as heart rate, movement, or other biometric signals. The system processes this data to determine the user's health state, which may include detecting anomalies, trends, or specific conditions. To improve efficiency, the system dynamically adjusts the sampling rate of one or more sensor channels based on the user's health state. This adjustment is performed iteratively or recursively, meaning the sampling rate is continuously refined as new data is collected and the health state is reassessed. For example, if the user's health state indicates a stable condition, the sampling rate may be reduced to conserve power, whereas an unstable or critical state may trigger higher sampling rates for more detailed monitoring. The system may also include preprocessing steps to filter or normalize sensor data before analysis, ensuring accurate health state assessments. The dynamic adjustment mechanism ensures that the system adapts to changing health conditions while maintaining optimal performance and resource usage.
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December 3, 2019
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